Analyzing and Navigating ETDs Using Topic Models

Abstract

Electronic theses and dissertations (ETDs) contain valuable knowledge that can be useful in a wide range of research areas. Accordingly, we are building electronic infrastructure leveraging advanced work on digital libraries, for discovering and accessing the knowledge buried in ETDs. In this paper we focus on our work to incorporate topic modeling into digital libraries for ETDs. We present ETD-Topics, a framework that extracts topics from a large text corpus in an unsupervised way. The representations learnt from topic models can be useful for downstream tasks such as searching and/or browsing documents by topic, document recommendation, topic recommendation, and describing temporal topic trends (e.g., from the perspective of disciplines or universities).

Citation

BibTeX

@misc{ahuja2022analyzing,
  author = {Ahuja, Aman and Ingram, William A. and Mao, Chenyu and He, Chongyu and Wei, Jianchi and Fox, Edward A.},
  title = {Analyzing and Navigating ETDs Using Topic Models},
  howpublished = {25th International Symposium on Electronic Theses and Dissertations},
  year = {2022},
  month = sep,
  url = {https://etd2022.uns.ac.rs/},
  location = {Novi Sad, Serbia},
  month_numeric = {9}
}